• International conference on construction engineering and project management
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• 2015.10a
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• pp.612-613
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• 2015

As a construction project is recently becoming large-scaled and complex, construction process plan and management for successful performance of a construction project has become more important. Especially a reasonable estimation plan of activity duration is required because the activity duration is directly related to the determination of the entire project duration and budget. However, the activity duration is used to estimate by the experience of a construction manager and past construction records. Furthermore, the prediction of activity duration is more difficult because there is some uncertainty caused by various influencing factors in a construction project. This study suggests an estimation model of construction activity duration using neural network theory for a more systematic and objective estimation of each activity duration. Because suggested model estimates the activity duration by a reasonable schedule plan, it is expected to reduce the error between planning duration and actual duration in a construction project. And it can be a more systematic estimation method of activity duration comparing to the estimation method by experience of project manager.

• Korean Journal of Construction Engineering and Management
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• v.5 no.6 s.22
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• pp.138-145
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• 2004

The construction duration for any building or facilities such as high school building influence the quality of the building as well as the total cost for them. Since there are no guidelines to estimate construction duration correctly, an employer(or owner) estimate it by their own experience or intuition. Therefore some conflicts related to construction duration happen between contract parties during construction. The purpose of this study is to suggest a predictive model which helps decision makers calculate exact net working days for high school building construction at the early stage of the construction project. To measure net working days for high school construction, 15 data were collected from actual spot in Incheon region. Multiple linear regression analysis was conducted to obtain the model which calculate construction duration for the substructure, the superstructure and the finishing works. total construction duration could be obtained by adding net working days to non working days which would be based on the meteorological statistics for Incheon region since 1974 to 2003.

• Journal of Advanced Marine Engineering and Technology
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• v.22 no.6
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• pp.809-815
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• 1998

This study has been performed to investigate MRR(metal removal rate) surface roughness with various pulse-on duration using the copper and graphite electrode according to the electrode size on the heat treated STD 11 which is extensively used for metallic molding steel in the EDM. The results obtained are as follow ;a)MRR increases a lot when pulse-on duration is 100 $\mu{s}$ or less but MRR has little difference with pulse-on duration of 100 $\mu{s}$ or more b) According to the increase of Pulse-on duration the large the electrode size the more MRR c) Safe discharge is needed to make maximum of MRR and the metallic organization must be complicated for discharge induction. d) Actual machining time is longer than theoretical machining time at the short pulse-on duration because of skin effect of current. e) Graphite electrode needs the larger electric discharge energy than copper electrode to remove remained chips completely.

• International conference on construction engineering and project management
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• 2022.06a
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• pp.608-615
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• 2022

Construction projects are large-scale projects that require extensive construction costs and resources. Especially, scheduling is considered as one of the essential issues for project success. However, the schedule and resource management are challenging to conduct in high-tech construction projects including complex design of MEP and architectural finishing which has to be constructed within a limited workspace and duration. In order to deal with such a problem, this study suggests resource and sequence optimization using constraint programming in construction projects. The optimization model consists of two modules. The first module is the data structure of the schedule model, which consists of parameters for optimization such as labor, task, workspace, and the work interference rate. The second module is the optimization module, which is for optimizing resources and sequences based on Constraint Programming (CP) methodology. For model validation, actual data of plumbing works were collected from a construction project using a five-minute rate (FMR) method. By comparing actual data and optimized results, this study shows the possibility of reducing the duration of plumbing works in construction projects. This study shows decreased overall project duration by eliminating work interference by optimizing resources and sequences within limited workspaces.

• KOREAN JOURNAL OF CROP SCIENCE
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• v.30 no.2
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• pp.131-139
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• 1985

Breeders have concerned with the nature of gene action controlling the duration of grain filling period to combine early maturity and acceptable grain yield to fit wheat into multiple cropping systems. The 4 x 4 complete diallel set of F$_1$, F$_2$ and 1/2 (BC$_1$ + BC$_2$) in crosses of winter and spring wheat cultivars was made to determine the nature of gene action involved for duration of grain filling period. Using the Jinks-Hayman model, no maternal effects were noted nor were any non-allelic interactions observed for total duration of grain filling and lag period. The actual grain filling period was influenced to some degree by such interactions. The spring cultivars Red Bobs and Siete Cerros also appeared to have more dominant genes for longer total duration of grain filling and lag period. In contrast, the winter parents Yamhill and Hyslop had more dominant genes for the longer actual grain filling period. The genes appeared to be independently distributed among the parents.

• Speech Sciences
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• v.10 no.4
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• pp.7-21
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• 2003

Sums-of-Products models were built for segment duration prediction of spoken Korean. An experiment for the modelling was carried out to apply the results to Korean text-to-speech synthesis systems. 670 read sentences were analyzed. trained and tested for the construction of the duration models. Traditional sequential rule systems were extended to simple additive, multiplicative and additive-multiplicative models based on Sums-of-Products modelling. The parameters used in the modelling include the properties of the target segment and its neighbors and the target segment's position in the prosodic structure. Two optimisation strategies were used: the downhill simplex method and the simulated annealing method. The performance of the models was measured by the correlation coefficient and the root mean squared prediction error (RMSE) between actual and predicted duration in the test data. The best performance was obtained when the data was trained and tested by ' additive-multiplicative models. ' The correlation for the vowel duration prediction was 0.69 and the RMSE. 31.80 ms. while the correlation for the consonant duration prediction was 0.54 and the RMSE. 29.02 ms. The results were not good enough to be applied to the real-time text-to-speech systems. Further investigation of feature interactions is required for the better performance of the Sums-of-Products models.

• Asia pacific journal of information systems
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• v.34 no.1
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• pp.226-247
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• 2024

Most studies on recommender systems to evaluate recommendation performances focus on offline evaluation methods utilizing past customer transaction records. However, evaluating recommendation performance through real-world stimulation becomes challenging. Moreover, such methods cannot evaluate the duration of the recommendation effect. This study measures the personalized recommendation (stimulus) effect when the product recommendation to customers leads to actual purchases and evaluates the duration of the stimulus personalized recommendation effect leading to purchases. The results revealed a 4.58% improvement in recommendation performance in the online environment compared with that in the offline environment. Furthermore, there is little difference in recommendation performance in offline experiments by period, whereas the recommendation performance declines with time in online experiments.